Active Sensor Planning for Multiview Vision Tasks [electronic resource] / edited by Shengyong Chen, Y. F. Li, Jianwei Zhang, Wanliang Wang.

By: Chen, Shengyong [editor.]Contributor(s): Li, Y. F [editor.] | Zhang, Jianwei [editor.] | Wang, Wanliang [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Description: online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540770725Subject(s): Engineering | Artificial intelligence | Computer vision | Systems theory | Engineering | Automation and Robotics | Control Engineering | Artificial Intelligence (incl. Robotics) | Systems Theory, Control | Signal, Image and Speech Processing | Image Processing and Computer VisionAdditional physical formats: Printed edition:: No titleOnline resources: Click here to access online
Contents:
Active Vision Sensors -- Active Sensor Planning – the State-of-the-Art -- Sensing Constraints and Evaluation -- Model-Based Sensor Planning -- Planning for Freeform Surface Measurement -- Sensor Planning for Object Modeling -- Information Entropy Based Planning -- Model Prediction and Sensor Planning -- Integrating Planning with Active Illumination.
In: Springer eBooksSummary: Vision sensors have limited fields of views and can only "see" a portion of a scene from a single viewpoint. To make the entire object visible, the sensor has to be moved from one place to another around the object to observe all features of interest, which brings a multiview vision task that has to be solved by means of active perception. The sensor planning presented in this book describes some effective strategies to generate a sequence of viewing poses and sensor settings for optimally completing a perception task. Several methods are proposed to solve the problems in both model-based and nonmodel-based vision tasks. For model-based applications, the method involves determination of the optimal sensor placements and a shortest path through these viewpoints for automatic generation of a perception plan. For nonmodel-based applications, the method involves determination of the best next view and sensor settings, to incrementally acquire the object information and to find geometrical cues to predict the unknown portion of an object or environment. The ten chapters in Active Vision Planning draw on recent work in robot vision over ten years, particularly in the use of new concepts of active sensing, reconfiguration, recalibration, sensor modeling, sensing constraints, sensing evaluation, viewpoint decision, sensor placement graph, model based planning, path planning, planning for robot in unknown environment, dynamic 3D construction, surface prediction, etc. Implementation examples are also provided with theoretical methods for testing in a real robot system. With these optimal sensor planning strategies, this book will give the robot vision system the adaptability needed in many practical applications.
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Active Vision Sensors -- Active Sensor Planning – the State-of-the-Art -- Sensing Constraints and Evaluation -- Model-Based Sensor Planning -- Planning for Freeform Surface Measurement -- Sensor Planning for Object Modeling -- Information Entropy Based Planning -- Model Prediction and Sensor Planning -- Integrating Planning with Active Illumination.

Vision sensors have limited fields of views and can only "see" a portion of a scene from a single viewpoint. To make the entire object visible, the sensor has to be moved from one place to another around the object to observe all features of interest, which brings a multiview vision task that has to be solved by means of active perception. The sensor planning presented in this book describes some effective strategies to generate a sequence of viewing poses and sensor settings for optimally completing a perception task. Several methods are proposed to solve the problems in both model-based and nonmodel-based vision tasks. For model-based applications, the method involves determination of the optimal sensor placements and a shortest path through these viewpoints for automatic generation of a perception plan. For nonmodel-based applications, the method involves determination of the best next view and sensor settings, to incrementally acquire the object information and to find geometrical cues to predict the unknown portion of an object or environment. The ten chapters in Active Vision Planning draw on recent work in robot vision over ten years, particularly in the use of new concepts of active sensing, reconfiguration, recalibration, sensor modeling, sensing constraints, sensing evaluation, viewpoint decision, sensor placement graph, model based planning, path planning, planning for robot in unknown environment, dynamic 3D construction, surface prediction, etc. Implementation examples are also provided with theoretical methods for testing in a real robot system. With these optimal sensor planning strategies, this book will give the robot vision system the adaptability needed in many practical applications.

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